We then proceed to compare our execution stage to popular conditional semantic generation models, including the aforementioned SBGAN and also: (8) Pix2PixHD [1]} which uses a U-Net [2]} to translate source to target images, (9) BicycleGAN [3]} that promotes cycle consistency among domains, and (10) SPADE [4]}, which performs spatial modualtion using a fixed set of trainable semantic category vectors. For disentanglement and controllability experiments, we compare the GANformer2 to a baseline GAN, StyleGAN2 and GANformer, as well as: (11) MONet and (12) Iodine, two sequential variational autoencoders.
